TITLE: Information Filtering and arXiv.org: Bandits, Exploration vs. Exploitation, and the Cold Start Problem
SPEAKER: Peter Frazier, Cornell University
We consider information filtering, in which we face a stream of items too voluminous to process by hand (e.g., scientific articles, blog posts, emails), and must rely on a computer system to automatically filter out irrelevant items. Such systems face the exploration vs. exploitation tradeoff, in which it may be beneficial to present an item despite a low probability of relevance, just to learn about future items with similar content. We present a Bayesian sequential decision-making model of this problem, and provide a decomposition technique that allows solving the resulting Markov decision process to optimality. We show that the resulting method is especially useful when facing the cold start problem, i.e., when filtering items for new users without a long history of past interactions. We then present initial results from an ongoing rollout of this information filtering method for an influential repository of scientific articles, arXiv.org.
- Workflow Status: Published
- Created By: Anita Race
- Created: 04/01/2014
- Modified By: Fletcher Moore
- Modified: 04/13/2017